Abstract:
Despite most organisations adopting big data analytics capabilities as a strategic tool to navigating the highly competitive market environment and remaining competitive through tailor-made customer solutions, operational efficiencies and effective decision-making, only a limited number of organisations have benefited from big data-analytics investment and deployment. Thus, with the recorded success of companies like Amazon, Wal-Mart, Netflix and others, it became imperative for businesses to deconvolute the requirements for successful big data analytics deployment and value creation. Due the accelerated growth in data volume, variety, velocity and veracity much attention and research in recent times has focused on technical requirements for big data analytics capabilities value creation. However, even with the vast research data readily available on technical skills, technological capabilities and applications most organisations continue to grabble with extracting value from data at their disposal. This implies that there are other dimensions that contribute to big data analytics success in firms. Until these dimensions are fully understood individually or collectively by firms, value creation will continue to remain a challenge in this context.
The research described herein therefore shifted focus from technical to non-technical capabilities and traits that an organisation should acquire to succeed with big data analytics capabilities. Consequently, the research used quantitative multivariate analysis to study the relationship between big data analytics application, knowledge or insights sharing and firm performance moderated by non-technical organisational factors such as organisational culture to decision making and entrepreneurial orientation.
The research thus found a positive correlation between knowledge sharing and business performance but organisational culture and entrepreneurial orientation even though showing primarily a positive effect on firm performance showed insignificant moderating effect on knowledge sharing. This therefore suggests that if organisations want to impact performance they must first align their knowledge sharing variables and moderating factors relevantly to be effective in big data analytics value creation.
